Pub Date : 2017-11-11DOI: 10.11648/j.sjams.20170506.11
Wambua Alex Mwaniki, Koske Joseph, Mutiso John, M. Wellington, K. Catherine, Eboi Bramuel
Agriculture and its related economic activities form the main livelihood for Kenya population. The sector faces numerous challenges that have led to food insecurity in the country. Maize production plays a significant role in the country’ economic development contributing significantly to the national overall Gross Domestic Product (GDP). Declining maize grain yield is one of the major challenges that require interventions to avert the looming food crisis. To address the challenge various Long Term Agricultural Experiments (LTAE) and studies on soil fertility maintainance options have been developed. However, such studies have explored only single factors at a time with limited application of robust statistical application. Statistical procedures could offer best set of few treatment factors that explain the maize grain yields in LTAEs in Kenya and beyond. The focus of this paper was the application of robust statistical methods in obtaining set of minimum treatment factors that could be used in the determination maize grain yield in LTAE. Specifically, the paper sought to describe the trend in maize grain yield over the experimental period, characterize the input factors for maize grain yield and to determine the most significant treatment factors for maize grain yield and total microbial population count (bacteria, fungi, actinomycetes, rhizobia). The primary data was summarized from LTAE in National Agricultural Research Laboratories (NARL), Kabete under the Kenya Agriculture and Livestock Research Organization (KALRO) and secondary data imputed for experimental points falling outside the set field experimental design points. Two treatment factors were isolated (Farm Yard Manure (FYM) and Nitrogen and Phosphorus (NP)) at their low factor levels as the most significant treatment factor in maximizing the maize grain yield and total microbial population count. It was possible to select a minimum set of treatment factors in LTAE that are critical in predicting the maize grain yield.
{"title":"Evaluation of the Most Significant Treatment Factors for Maize Grain Yields and Total Microbial Count in Long Term Agricultural Experiment (LTAE), Kenya","authors":"Wambua Alex Mwaniki, Koske Joseph, Mutiso John, M. Wellington, K. Catherine, Eboi Bramuel","doi":"10.11648/j.sjams.20170506.11","DOIUrl":"https://doi.org/10.11648/j.sjams.20170506.11","url":null,"abstract":"Agriculture and its related economic activities form the main livelihood for Kenya population. The sector faces numerous challenges that have led to food insecurity in the country. Maize production plays a significant role in the country’ economic development contributing significantly to the national overall Gross Domestic Product (GDP). Declining maize grain yield is one of the major challenges that require interventions to avert the looming food crisis. To address the challenge various Long Term Agricultural Experiments (LTAE) and studies on soil fertility maintainance options have been developed. However, such studies have explored only single factors at a time with limited application of robust statistical application. Statistical procedures could offer best set of few treatment factors that explain the maize grain yields in LTAEs in Kenya and beyond. The focus of this paper was the application of robust statistical methods in obtaining set of minimum treatment factors that could be used in the determination maize grain yield in LTAE. Specifically, the paper sought to describe the trend in maize grain yield over the experimental period, characterize the input factors for maize grain yield and to determine the most significant treatment factors for maize grain yield and total microbial population count (bacteria, fungi, actinomycetes, rhizobia). The primary data was summarized from LTAE in National Agricultural Research Laboratories (NARL), Kabete under the Kenya Agriculture and Livestock Research Organization (KALRO) and secondary data imputed for experimental points falling outside the set field experimental design points. Two treatment factors were isolated (Farm Yard Manure (FYM) and Nitrogen and Phosphorus (NP)) at their low factor levels as the most significant treatment factor in maximizing the maize grain yield and total microbial population count. It was possible to select a minimum set of treatment factors in LTAE that are critical in predicting the maize grain yield.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114755116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-08DOI: 10.11648/J.SJAMS.20170505.13
Y. S. Awari
A Modified Three Step Block Hybrid Extended Trapezoidal Multistep Method of Second Kind (BHETR 2 s) with two off-grid points, one at interpolation and another at collocation point yielding uniform order six (6, 6, 6, 6, 6) T for the Numerical Integration of initial value problems of stiff Ordinary Differential Equations was developed. The main method and additional equations were obtained from the same continuous formulation through interpolation and collocation procedures. The stability properties of the method was discussed and from the stability region obtained, the method is suitable for the solution Stiff Ordinary Differential Equations. Three numerical examples were considered to illustrate the efficiency and accuracy.
{"title":"Numerical Strategies for the System of First Order IVPs Using Block Hybrid Extended Trapezoidal Multistep Method of Second Kind for Stiff ODEs","authors":"Y. S. Awari","doi":"10.11648/J.SJAMS.20170505.13","DOIUrl":"https://doi.org/10.11648/J.SJAMS.20170505.13","url":null,"abstract":"A Modified Three Step Block Hybrid Extended Trapezoidal Multistep Method of Second Kind (BHETR 2 s) with two off-grid points, one at interpolation and another at collocation point yielding uniform order six (6, 6, 6, 6, 6) T for the Numerical Integration of initial value problems of stiff Ordinary Differential Equations was developed. The main method and additional equations were obtained from the same continuous formulation through interpolation and collocation procedures. The stability properties of the method was discussed and from the stability region obtained, the method is suitable for the solution Stiff Ordinary Differential Equations. Three numerical examples were considered to illustrate the efficiency and accuracy.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"90 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131193141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-05DOI: 10.11648/J.SJAMS.20170505.12
O. Adubisi, Titus Terkaa Mom, C. Adubisi, Phillip Luka
In the last few decades, crude oil has claimed the topmost position in Nigerian export list, constituting a very fundamental change in the structure of Nigerian international trade. In this study, secondary data on monthly crude oil export to the United States was obtained from the Energy Information Administration (EIA) database. Using the Box-Jenkins (ARIMA) methodology, the results showed that Seasonal ARIMA (0, 1, 1) (1, 0, 1)12 model had the least information criteria after the data was Square-Root transformed and non-seasonally first differenced in order to achieve series stationarity. The diagnostic tests on the selected model residuals revealed the residuals are normally distributed uncorrelated random shocks.
{"title":"Predictive Model with Square-Root Variance Stabilizing Transformation for Nigeria Crude Oil Export to America","authors":"O. Adubisi, Titus Terkaa Mom, C. Adubisi, Phillip Luka","doi":"10.11648/J.SJAMS.20170505.12","DOIUrl":"https://doi.org/10.11648/J.SJAMS.20170505.12","url":null,"abstract":"In the last few decades, crude oil has claimed the topmost position in Nigerian export list, constituting a very fundamental change in the structure of Nigerian international trade. In this study, secondary data on monthly crude oil export to the United States was obtained from the Energy Information Administration (EIA) database. Using the Box-Jenkins (ARIMA) methodology, the results showed that Seasonal ARIMA (0, 1, 1) (1, 0, 1)12 model had the least information criteria after the data was Square-Root transformed and non-seasonally first differenced in order to achieve series stationarity. The diagnostic tests on the selected model residuals revealed the residuals are normally distributed uncorrelated random shocks.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125957685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-22DOI: 10.11648/j.sjams.20170505.11
R. Simwa, Nelson Lwoyelo Muhati, L. Chikamai
Human Immunodeficiency Virus (HIV) and Mycobacterium Tuberculosis (TB) infections are two major world’s public health problems especially in developing countries. Worldwide, 13% of TB cases are estimated to be co-infected with HIV and about a third of 33 million people living with HIV are infected with the bacterium that causes TB. Deterministic models are derived and applied to estimate the basic reproduction number of HIV and TB co-infection as a single output value by treating each of the parameter input as a constant value. In this paper the basic reproduction number is modeled as a random variable, then the probability that there will bean epidemic, is derived and computed. In particular it is shown that for the sub-Saharan region, the probability of the epidemic occurring is 7.9%, as expected since an epidemic is generally a rare event. This research, thus develops the methodology for the computation of the probability of occurrence of an epidemic, which is useful for the public health policy formulation.
{"title":"On Derivation of the Probability of Occurrence of an Epidemic with Application to HIV/AIDS Spread Given Tuberculosis Co-infections in the Presence of Treatment","authors":"R. Simwa, Nelson Lwoyelo Muhati, L. Chikamai","doi":"10.11648/j.sjams.20170505.11","DOIUrl":"https://doi.org/10.11648/j.sjams.20170505.11","url":null,"abstract":"Human Immunodeficiency Virus (HIV) and Mycobacterium Tuberculosis (TB) infections are two major world’s public health problems especially in developing countries. Worldwide, 13% of TB cases are estimated to be co-infected with HIV and about a third of 33 million people living with HIV are infected with the bacterium that causes TB. Deterministic models are derived and applied to estimate the basic reproduction number of HIV and TB co-infection as a single output value by treating each of the parameter input as a constant value. In this paper the basic reproduction number is modeled as a random variable, then the probability that there will bean epidemic, is derived and computed. In particular it is shown that for the sub-Saharan region, the probability of the epidemic occurring is 7.9%, as expected since an epidemic is generally a rare event. This research, thus develops the methodology for the computation of the probability of occurrence of an epidemic, which is useful for the public health policy formulation.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123450423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-26DOI: 10.11648/j.sjams.20170504.15
Runyi E. Francis
This study investigates and models salient factors that influences the performance of staff in an appraisal exercise and as well estimate the odds of these factors influencing the outcome variable (performance rating) as compared to their reference group or category. The Binary Logistic regression model was used to estimate chance of the staff given the influence of the identified independent variables. In the study, marital status was found to be significant in distinguishing staff performance as identified from the outlined factors influencing their performance.
{"title":"The Application of Binary Logistic Regression Analysis on Staff Performance Appraisal","authors":"Runyi E. Francis","doi":"10.11648/j.sjams.20170504.15","DOIUrl":"https://doi.org/10.11648/j.sjams.20170504.15","url":null,"abstract":"This study investigates and models salient factors that influences the performance of staff in an appraisal exercise and as well estimate the odds of these factors influencing the outcome variable (performance rating) as compared to their reference group or category. The Binary Logistic regression model was used to estimate chance of the staff given the influence of the identified independent variables. In the study, marital status was found to be significant in distinguishing staff performance as identified from the outlined factors influencing their performance.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124082292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-25DOI: 10.11648/J.SJAMS.20170504.14
E. Ouedraogo, B. Somé, S. Dossou-Gbété
This paper deals with a Maximum likelihood method to fit a three-parameter gamma distribution to data from an independent and identically distributed scheme of sampling. The likelihood hinges on the joint distribution of the n − 1 largest order statistics and its maximization is done by resorting to a MM-algorithm. Monte Carlo simulations is performed in order to examine the behavior of the bias and the root mean square error of the proposed estimator. The performances of the proposed method is compared to those of two alternatives methods recently available in the literature: the location and scale parameters free maximum likelihood estimators (LSPF-MLE) of Nagatsuka & al. (2014), and Bayesian Likelihood (BL) method of Hall and Wang (2005). As in several papers on the three-parameter gamma fitting (Cohen and Whitten (1986), Tzavelas (2009), Nagatsuka & al. (2014), etc.), the classical dataset on the maximum flood levels data in millions of cubic feet per second for the Susquehanna River at Harrisburg, Pennsylvania, over 20 four-year periods from 1890–1969 from Antle and Dumonceaux’s paper (1973) is consider to illustrate the proposed method.
{"title":"On Maximum Likelihood Estimation for the Three Parameter Gamma Distribution Based on Left Censored Samples","authors":"E. Ouedraogo, B. Somé, S. Dossou-Gbété","doi":"10.11648/J.SJAMS.20170504.14","DOIUrl":"https://doi.org/10.11648/J.SJAMS.20170504.14","url":null,"abstract":"This paper deals with a Maximum likelihood method to fit a three-parameter gamma distribution to data from an independent and identically distributed scheme of sampling. The likelihood hinges on the joint distribution of the n − 1 largest order statistics and its maximization is done by resorting to a MM-algorithm. Monte Carlo simulations is performed in order to examine the behavior of the bias and the root mean square error of the proposed estimator. The performances of the proposed method is compared to those of two alternatives methods recently available in the literature: the location and scale parameters free maximum likelihood estimators (LSPF-MLE) of Nagatsuka & al. (2014), and Bayesian Likelihood (BL) method of Hall and Wang (2005). As in several papers on the three-parameter gamma fitting (Cohen and Whitten (1986), Tzavelas (2009), Nagatsuka & al. (2014), etc.), the classical dataset on the maximum flood levels data in millions of cubic feet per second for the Susquehanna River at Harrisburg, Pennsylvania, over 20 four-year periods from 1890–1969 from Antle and Dumonceaux’s paper (1973) is consider to illustrate the proposed method.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"56 37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122272949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-19DOI: 10.11648/j.sjams.20170504.13
Yu-E. Bao, Yingfang Niu, Yuan Li
In this paper, we firstly discuss the basic properties of the sub differential of fuzzy mapping and get some related conclusions. Secondly, we establish a variational principle of fuzzy mapping by establishing the concept of gauge fuzzy mapping. Then we prove the approximation sun rule of fuzzy mapping in sub-differential as the application of that principles.
{"title":"Variational Principles of Fuzzy Mappings and Its Applications","authors":"Yu-E. Bao, Yingfang Niu, Yuan Li","doi":"10.11648/j.sjams.20170504.13","DOIUrl":"https://doi.org/10.11648/j.sjams.20170504.13","url":null,"abstract":"In this paper, we firstly discuss the basic properties of the sub differential of fuzzy mapping and get some related conclusions. Secondly, we establish a variational principle of fuzzy mapping by establishing the concept of gauge fuzzy mapping. Then we prove the approximation sun rule of fuzzy mapping in sub-differential as the application of that principles.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131632732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-07DOI: 10.11648/J.SJAMS.20170504.11
Markos Abiso Erango, A. Goshu
The aim of this paper is to predict and compare the survival of HIV/AIDS patients under ART follow-up in three different hospitals in Ethiopia. Three data sets with total1304 patients were considered.Three parametric accelerated failure time distributions: lognormal, loglogistic and Weibull are used to analyze, predict and compare survival probabilities of the patients. The results indicate that the empirical hazard rates of the three data sets reveal maximal peaks. The patients from Arba Minch hospital seems to have highest event intensity. The AFT loglogistic model is selected to best fit to each of the data sets.Different covariates except TB infection status are found to affect patients' survival at each of the hospitals. Patients with TB infection at baseline tend to have shorter survival time as compare to one with no TB infection, with significant differences of survive time between the two groups. Patients under follow-up at Shashemene hospital tend have consistently highest survival probabilities in both TB positive and negative groups. Patients from Bale Robe hospital tend to have longest survival time, while those from Arba Minch hospital have shortest survival time.Patients with bedridden status have the shortest survival time.The AFT-loglogistic is recommended in modelling time-to-event data considered in this study. The results are unique to each hospital implying that patients' care and intervention needs to be specific.
{"title":"Prediction of Survival of HIV/AIDS Patients from Various Sources of Data Using AFT Models","authors":"Markos Abiso Erango, A. Goshu","doi":"10.11648/J.SJAMS.20170504.11","DOIUrl":"https://doi.org/10.11648/J.SJAMS.20170504.11","url":null,"abstract":"The aim of this paper is to predict and compare the survival of HIV/AIDS patients under ART follow-up in three different hospitals in Ethiopia. Three data sets with total1304 patients were considered.Three parametric accelerated failure time distributions: lognormal, loglogistic and Weibull are used to analyze, predict and compare survival probabilities of the patients. The results indicate that the empirical hazard rates of the three data sets reveal maximal peaks. The patients from Arba Minch hospital seems to have highest event intensity. The AFT loglogistic model is selected to best fit to each of the data sets.Different covariates except TB infection status are found to affect patients' survival at each of the hospitals. Patients with TB infection at baseline tend to have shorter survival time as compare to one with no TB infection, with significant differences of survive time between the two groups. Patients under follow-up at Shashemene hospital tend have consistently highest survival probabilities in both TB positive and negative groups. Patients from Bale Robe hospital tend to have longest survival time, while those from Arba Minch hospital have shortest survival time.Patients with bedridden status have the shortest survival time.The AFT-loglogistic is recommended in modelling time-to-event data considered in this study. The results are unique to each hospital implying that patients' care and intervention needs to be specific.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114849752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-07DOI: 10.11648/j.sjams.20170504.12
L. G. L. Jumi
Malaria is a leading cause of morbidity and mortality in South Sudan. This study is meant to focus on the trend of malaria incidence in Jubek state, South Sudan. Data on weekly malaria incidence for the period January 2011 to October 2015 were used in the study. Generalized linear models, Poisson and negative binomial regression models were employed to analyze the data. Results obtained suggest that malaria incidence has been still on increase by 0.0030 and 0.0032 per week respectively. Additionally, incidence rate ratio suggests an increase of 0.3% per week of malaria incidence in Jubek state. The study recommends malaria control programmes focused on reducing malaria incidence be introduced in South Sudan in general and in Jubek state in particular.
{"title":"Generalized Linear Models of Malaria Incidence in Jubek State, South Sudan","authors":"L. G. L. Jumi","doi":"10.11648/j.sjams.20170504.12","DOIUrl":"https://doi.org/10.11648/j.sjams.20170504.12","url":null,"abstract":"Malaria is a leading cause of morbidity and mortality in South Sudan. This study is meant to focus on the trend of malaria incidence in Jubek state, South Sudan. Data on weekly malaria incidence for the period January 2011 to October 2015 were used in the study. Generalized linear models, Poisson and negative binomial regression models were employed to analyze the data. Results obtained suggest that malaria incidence has been still on increase by 0.0030 and 0.0032 per week respectively. Additionally, incidence rate ratio suggests an increase of 0.3% per week of malaria incidence in Jubek state. The study recommends malaria control programmes focused on reducing malaria incidence be introduced in South Sudan in general and in Jubek state in particular.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121706591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-06-03DOI: 10.11648/J.SJAMS.20170503.13
S. Wiradinata, Rado Yendra, Suhartono, M. Gamal
This article discusses multi-input intervention analysis to investigate the effect of interventions which may come from internal and/or external factors in time series data. The objective of this research is to obtain multi-input intervention analysis, which can explain the magnitude and periodic of each event effected to monthly types of the domestic airline passenger flight in Pekanbaru airport. The purpose of this study is to give a theoretical and empirical studies on the multi-input intervention analysis, particularly to develop and construct a model procedure of multi-input intervention cused by pulse and/or step function to evaluate the impact of these external and/or internal events in time series data. Monthly data comprising the number of the domestic airline passenger flight in Pekanbaru airport are used as the data for this case study. Generally, the forest fires, peatland, and illegal burning in Riau Province give a negative permanent impacts after four months. This study focuses on the derivation of some effect shapes, i.e. the temporary, gradually or permanent monthly airline passenger. In addition, the research also discusses how to assess the effect of an intervention in transformation data.
{"title":"Multi-Input Intervention Analysis for Evaluating of the Domestic Airline Passengers in an International Airport","authors":"S. Wiradinata, Rado Yendra, Suhartono, M. Gamal","doi":"10.11648/J.SJAMS.20170503.13","DOIUrl":"https://doi.org/10.11648/J.SJAMS.20170503.13","url":null,"abstract":"This article discusses multi-input intervention analysis to investigate the effect of interventions which may come from internal and/or external factors in time series data. The objective of this research is to obtain multi-input intervention analysis, which can explain the magnitude and periodic of each event effected to monthly types of the domestic airline passenger flight in Pekanbaru airport. The purpose of this study is to give a theoretical and empirical studies on the multi-input intervention analysis, particularly to develop and construct a model procedure of multi-input intervention cused by pulse and/or step function to evaluate the impact of these external and/or internal events in time series data. Monthly data comprising the number of the domestic airline passenger flight in Pekanbaru airport are used as the data for this case study. Generally, the forest fires, peatland, and illegal burning in Riau Province give a negative permanent impacts after four months. This study focuses on the derivation of some effect shapes, i.e. the temporary, gradually or permanent monthly airline passenger. In addition, the research also discusses how to assess the effect of an intervention in transformation data.","PeriodicalId":422938,"journal":{"name":"Science Journal of Applied Mathematics and Statistics","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116436932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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